2. HIRLAM-6, development since last time Strategy - ALADIN - MF - collaboration Data assimilation, 3D/4D-VAR, surface Observation Usage Parameterisation – –turbulence and convection –Surface and radiation Physics coupling - boundary conditions Meso-scale modelling EPS Regular Cycle with the Reference (FMI)

3. HIRLAM-6 Memorandum of Understanding Targets –achieve highest possible accuracy for severe weather and of wind, precipitation and temperature –develop 3D/4D-VAR further and its use of non- conventional data –maintain the regular analysis/forecasting cycle –continue development of synoptic model 10-20 km –develop meso-scale non-hydrostatic operational model with suitable physical parameterisation –Overhaul of complete System –develop methods for probabilistic forecasting –continue development of verification methods

4. HIRLAM strategy - synoptic Synoptic model, 10-20 km, every 6 hours -> 2 (3) days, 4D-VAR and satellite data over a (fairly) large area –provides comprehensive set of forecast parameters for applications and driving other models –boundary conditions and tight coupling to meso-scale model –covers window between ECMWF forecasts - more recent observations and boundaries (frames)

5. HIRLAM strategy - meso-scale Meso-scale data assimilation and model, 2-3 km non- hydrostatic model +3-12 (24 h) –physics for 2km, explicit convection –turbulence and radiation non-local (later, ~ 1 km ) –rapid update cycle, vast amount of regional data available, conv/non-conv, reflectivity, precipitation.. –4D-VAR /3D-VAR FGAT - if in short time - spinup? –Boundary field impact, transparent boundary conditions !

6. HIRLAM strategy - meso-scale

8. HIRLAM research profile Physics interfaces - combinations –HIRLAM physics / AROME physics Synoptic physics HIRLAM/ALARO Synoptic 4D-VAR - migrate to ALARO Meso-scale 4D-VAR Meso-scale basis functions - J b - Observations - radar winds, surface, refl. Cloud, Large scale coupling - spectral - extension zone Meso-scale validation Probabilities with EPS and physical perturbations Surface modelling and assimilation (SST)

9. HIRLAM meso-scale group Learning - set up of ALADIN - climate - coupling DMI-SMHI-FMI-INM - Set up of domain(s) Physics interface - temporary - general HIRLAM and AROME First experiments Coupling with HIRLAM outer model

10. Data assimilation -3D-VAR 3D-VAR background constraint J b : –(x b - H(y)) T B -1 (x b - H(y)), sigma-b, horizontal variation, new structure functions => Background check, analysis increments Analytical balance (enh) ->statistical balance

11. 3D-VAR (cont) FGAT - First Guess at Appropriate Time

12. 4D-VAR Data Assimilation Adjoints of semi-Lagrangian spectral model Multi-incremental minimisation - low resolution Optimisations of transforms –> significant gain in economy, feasible for operations

13. 4D-VAR single obs 3 Dec 99 06-12 3 Dec 06 ->3 Dec 12

14. 4D-VAR argument Optimal solution in time including all information Iterativ method enabels non-linear operators - possible in 3D too, but : Non-linear analysis can transfer a vortex The model analyses non-observed quantaties Possible to use integrated observations Enables high time resolution of data and time sequence can be utilised - e.g. radar Model generated structure functions necessary for meso-scale

15. 4D-VAR Estimated computer requirements of SL incremental 4D-VAR Estimated cost of SL incremental 4D-VAR

16. 4D-VAR activity now Jc DFI - control of noise - NNMI in iterations Optimisation Multi-incremental and real trials 120 - 45 km minimisation, 22 - 17 km fcs about 1 hour for very large area

17. Analysis of surface parameters OI SST and Ice analysis –Ocean Sea Ice SAF data - New OI snow analysis ready for implementation –QC and bias correction (due to height differences) Tuning of 2m T och RH analysis (statistics) Old New

18. New Snow analysis SSM/I will help – LAND SAF data -

19. Observation Usage Conventional data –radiosonde launch times –radiosonde drift –comparing observation availability Remote sensing data –AMSU-A –AMSU-B –QuikScat –Radar doppler winds –GPS ZTD –WINDPROFILER

22. Reference caseGPS includedRadar 20020712_06 (analysis time)

24. Forecast Model - parameterisation Turbulence (CBR TKE-l) –Much attention to stable case - more mixing at high stability - modified - cut - smooth Ri >1 –Increased roughness - vegetational - orografical –Direction of surface stress vector –=> filling of lows, reduce 10 m wind –Moist conservative and moist stability version effect of condensation on stability

25. Stable stratification - increased mixing

26. Increased vegetational roughness

27. Turning of wind stress

28. Turning of wind stress II

29. Turning of stress and smooth mixing (Tijm, 2004)

30. Snow scheme in ISBA main modifications to original code: Only new snow scheme on fractions 3 and 4 and now 5 Force-restore formulation replaced by heat conduction Heat capacity of uppermost layer replaced by 1 cm moist soil. A second soil layer (7.2 cm) Forest area decreased so that at least 10% of area is low-vegetation At present (temporarily!) no soil freezing Forest tile, being developed - canopy snow and ground

31. Tclim ISBA: snow covering parts of fractions 3 and 4 Td snow Td 3 and 4 Ts2 snow Ts2 3 and 4 Ts snow Ts 3 and 4 T snow Thermally active layer snow in beginning of timestep Snow change mixing of T in soil between timesteps Features of the snow scheme: move the snow from fractions 3 and 4 to fraction 6 every timestep one layer of the snow, with a thermally active layer < 15 cm water in the snow, which can refreeze varying albedo and density mirroring of temperature profile in the ground to assure correct memory

34. Soil moisture adapts in assimilation to different vegetation types

35. Radiation and snow cover Soil Freezing - implemented e sat for ground <0  for ice implemented e sat over water and ice following K-I Ivarsson distribution water - ice in clouds to be consistent - large effect on emissivity - implemented radiation for sloping ground calculated - for HR

36. Radiation and condensation

37. Convection - condensation Kain-Fritsch Rash-Kristjanson –extensive tests and verification at 22 km better humidity –11 km indicates better results –Expensive, and very much so, on vector systems –Possible vectorised version

38. Model dynamics and embedding Coupling between SL advection and physics Semi-Lagrangian mods for orography (T eq.) Boundary relaxation (Host orography, interp.) Development of transparent boundary conditions Incremental Digital Filter Initialisisation Ensemble forecasts with HIRLAM Verification methods - meso-scale - Workshop Climate system developments System - upgrades - Reference test - RCR Communication - HeXNeT - RCR monitoring

39. Tanguy-Ritchie SL T-equation, SL extr

40. Transparent Boundary conditions

41. Transparent LBC progress 2D-shallow water model - several results 3D-simplest 2 layer baroclinic 3D-multilevel Z - –eigenvalues - Laplace transform –demonstrated 3D-mulitlevel eta - to be done Spectral LAM - extension zone - programming ?

42. New HR rotated climate data sets 0.0250.0125

44. Conclusions Systematic near surface errors adressed and worked on –turbulence, surface scheme, radiation-clouds New orientation towards Meso-scale Collaboration with ALADIN 4D-VAR for synoptic scales More remote sensing Lateral Boundary conditions developing - necessary Monitoring and quality of Reference system

45. Bias corrected

46. SMHI HIRLAM - 11 km -> HR-FAR

47. SMHI HIRLAM - Dec -> HR-FAR

48. Effect from esat condensation och radiation